The relationship between ethical issues and big data has long been a slippery slope that could lead to misunderstandings that this teaching seeks instead to unmask and avert : 1 that “ethical” issues are fundamentally normative or even prescriptive philosophical assumptions and that 2 data describe realities in themselves as if the empirical moment were distinct from other steps in cognitive processes. Through the general theory of complex evolutionary social systems we will see how philosophical-legal ethical responses are almost always inefficient and wasteful from every point of view as well as the illusion that the data returns reality per se. ethics and data , when they work create an evolutionary design strategy for the sicial complexity of knowledge intensive and high tech organizations. Such a design strategy constitutes the core of this teaching. Significantly, this implies taking note that both ethical modeling and big data are constructions that are semantically non Tarskian, open enough to allow for structural couplings that are not over- or under-interpreted. In other words, that ethics and big data are constructions that can be interfaced and thus co-designed at the same level of abstraction without falling into the perspectival error that the former drives the latter like a brain drives brute matter. In this sense, the most typical design error-which strongly emerges from cybersecurity policies-is to understand these as the new fortified walls and boiling pitch of a kind of digital neo-middle age as if existing institutions could manage the digital ceteris paribus